The use of sensors by various systems requires accurate calibration to be useful. For example, an autonomous vehicle may have multiple cameras, LIDAR sensors, radar sensors, and/or the like to detect objects, e.g., objects approaching the vehicle and/or objects the vehicle is approaching, and sensor data about those objects can be necessary to navigate relative to those objects. In some environments, including those in which other pedestrians, bicyclists, and other vehicles may be present, potentially fatal collisions may occur if such sensors are not calibrated properly. Current calibration techniques use infrastructure, such as fiducial markers, to calibrate sensors. For example, by capturing data of a fiducial marker, a correction term can be determined and applied to subsequently-captured data. While the infrastructure may be readily available at a location at which a system is manufactured or at other locations, subsequent calibration requires bringing the system (e.g. an autonomous vehicle) to a location that has infrastructure, resulting in undesirable downtime for the system and/or, for those examples which rely on sensors for navigation, potentially unsafe travel to the location. Additionally, current calibration techniques may require a human operator, which may make the process manual, slow, and potentially imprecise. Existing calibration techniques that attempt to mitigate these drawbacks are often computationally expensive.